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What are the critical assumptions of neuroscience?

by Neuroconscience

In light of all the celebration surrounding the discovery of a Higgs-like particle, I found it amusing that nearly 30 years ago Higg’s theory was rejected by CERN as ‘outlandish’. This got me to wondering, just how often is scientific consensus a bar to discovery? Scientists are only human, and as such can be just as prone to blindspots, biases, and herding behaviors as other humans. Clearly the scientific method and scientific consensus (e.g. peer review) are the tools we rely on to surmount these biases. Yet, every tool has it’s misuse, and sometimes the wisdom of the crowd is just the aggregate of all these biases.

At this point, David Zhou pointed out that when scientific consensus leads to rejection of correct viewpoints, it’s often due to the strong implicit assumptions that the dominant paradigm rests upon. Sometimes there are assumptions that support our theories which, due to a lack of either conceptual or methodological sophistication, are not amenable to investigation. Other times we simply don’t see them; when Chomsky famously wrote his review of Skinner’s verbal behavior, he simply put together all the pieces of the puzzle that were floating around, and in doing so destroyed a 20-year scientific consensus.

Of course, as a cognitive scientist studying the brain, I often puzzle over what assumptions I critically depend upon to do my work. In an earlier stage of my training, I was heavily inundated with ideas from the “embodied, enactive, extended” framework, where it is common to claim that the essential bias is an uncritical belief in the representational theory of mind. While I do still see problems in mainstream information theory, I’m no longer convinced that an essentially internalist, predictive-coding account of the brain is without merit. It seems to me that the “revolution” of externalist viewpoints turned out to be more of an exercise in house-keeping, moving us beyond overly simplistic “just-so” evolutionary diatribes,and empty connectionism, to introducing concepts from dynamical systems to information theory in the context of cognition.

So, really i’d like to open this up: what do you think are assumptions neuroscientists cannot live without? I don’t want to shape the discussion too much, but here are a few starters off the top of my head:

Nativism: informational constraints are heritable and innate, learning occurs within these bounds

Representation: Physical information is transduced by the senses into abstract representations for cognition to manipulate

Frequentism: While many alternatives currently abound, for the most part I think many mainstream neuroscientists are crucially dependent on assessing differences in mean and slope. A related issue is a tendency to view variability as “noise”

Mental Chronometry: related to the representational theory of mind is the idea that more complex representations take longer to process and require more resources. Thus greater (BOLD/ERP/RT) equals a more complex process.

Evolution: for a function to exist it must be selected for by evolutionary natural selection

That’s all off the top of my head. What do you think? Are these essential for neuroscience? What might a cognitive theory look like without these, and how could it motivate empirical research? For me, each of these are in some way quite helpful in terms of providing a framework to interpret reaction-time, BOLD, or other cognition related data. Have I missed any?

4 Comments to “What are the critical assumptions of neuroscience?”

Representation (in any kind of information processing sense) is not necessary, think of the dynamiscists. If you want to conceive of representation as something in the head that has some causal relation to the outside world then everything is representation but I think doing this is dangerous and over-inclusive.

Good question Micah but I have to ask this question first: What are the critical theories of neuroscience? Assumptions are, IMO, of little value (ie not critical) if they are not integral to successful models (ie theories).

Thanks for your comment! I suppose what I meant was, what are the background theories (assumptions) that neuroscience depends on. I agree that in general unexamined assumptions are not useful. But I think most neuroscientists don’t spend much time considering if what they measure are really “representations”, and if not what else they might “be”. Still I think that in a very practical way, underlying theoretical concepts or assumptions like representational theory of mind play an active role in facilitating data interpretation. We still work on the basic assumption that “more something” (time, blood, whatever)” = more computation/information.